AI-300 Generative AI Optimization Practice Question
You have a large set of documents for a RAG system. How should you optimize retrieval speed?
Answer choices
Why each option matters
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Store embeddings in a dedicated vector database with indexing.
Using a managed vector store with optimized indexing is essential for large-scale retrieval speed.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Store embeddings in a dedicated vector database with indexing.
Why this is correct
Vector databases provide optimized search algorithms for large datasets.
- ✗
Process all documents in the prompt.
Why it's wrong here
Prompt context limits make this impossible for large document sets.
- ✗
Increase the model's max_tokens.
Why it's wrong here
This doesn't impact retrieval speed.
- ✗
Perform a brute-force search on all documents.
Why it's wrong here
Brute-force (kNN) is too slow for large datasets.
About these practice questions
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JA
Written and reviewed by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
Last reviewed August 2026 · checked against the official Microsoft exam blueprint
This AI-300 practice question is part of Courseiva's free Microsoft certification practice question bank. Courseiva provides original exam-style practice questions with explanations, topic-based practice, mock exams, readiness tracking, and study analytics to help learners prepare for the AI-300 exam.